Reimagining urban air-quality governance: A systems-thinking framework
Bibliographic record
Abstract
Despite stringent ambient air quality standards, air pollution remains a persistent issue in many regions due to inadequate policy implementation at the local level. This reflects a form of policy resistance where misaligned stakeholder goals undermine otherwise well-intentioned governmental interventions, highlighting the need for realignment around common objectives to achieve meaningful progress. We apply a systems thinking approach to identify implementation gaps in air quality management, using India as a focal point. Adapting the Integrated Air Quality and Climate Change (IAQCC) framework, we construct causal loop diagrams (CLDs) to map the complex feedback relationships among policy enforcement, data infrastructure, stakeholder behavior, and health outcomes. Through a literature review and expert consultations, we identify three loops that perpetuate policy stagnation: (1) insufficient monitoring data hampers source identification, policy evaluation, and refinement, (2) fragmented jurisdictional authority hinders coordination among stakeholders, and (3) weak local compliance erodes public trust and engagement. All three loops stem from the absence of a unifying objective. We propose reorienting the system around a public health goal, such as reductions in premature mortality or asthma incidence, to align stakeholder incentives and reduce resistance to local policy implementation. We identify three key leverage points to enable this realignment: (1) establish integrated and accessible air-and-health data platforms, (2) coordinate across jurisdictional boundaries through airshed-based governance, and (3) empower citizen-science and community-led oversight. This approach shifts the focus from top-down technocratic management to participatory, democratic governance, showing how targeted, strategic interventions can produce improvements in air quality management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".